集成为图形神经网络方法的定向静电学策略,用于加速集群结构预测.
Sridatri Nandy1, K V Jovan Jose1
1Advanced Artificial Intelligence Theoretical and Computational Chemistry Laboratory, School of Chemistry, University of Hyderabad, Hyderabad, Telangana 500046, India.
Journal of chemical theory and computation
|January 15, 2025
概括
我们开发了一个图形神经网络 (DESIGNN),用定向静电学来预测稳定的纳米集群结构. 这种方法可以准确地识别的集群结构,并产生新的异构体,加速材料的发现.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 人工智能在预测原子结构方面的应用.
背景情况:
- 预测稳定的纳米集群结构在计算上具有挑战性.
- 了解潜在能量表面 (PES) 对于确定稳定的原子集群配置至关重要.
研究的目的:
- 引入一种基于图形神经网络 (GNN) 的新方法,DESIGNN,用于预测稳定的纳米集群结构.
- 利用定向静电学和分子静电潜能 (MESP) 地形学用于引导结构预测.
主要方法:
- 开发了 DESIGNN 方法,将图形神经网络与定向静电学集成在一起.
- 在 (Mg) 集群 (n < 150) 上对模型进行了基准测试.
- 预测的MESP地形最小值和对集群演变的分析家长增长潜力 (GP).
主要成果:
- 设计准确地预测MESP对Mg集群 (n <70) 的地形最小值,与完整的计算保持一致.
- 为Mg集群 (n = 4-32) 生成基态结构,与文献发现相匹配.
- 成功生成了新的对称异构体和大型稳定的Mg纳米集群 (n高达260).
结论:
- 设计方法有效地加速了稳定的纳米集群结构的搜索和预测.
- 这种方法显示出在以MESP地形为指导的大型金属集群的探索方面具有显著的前景.
- 设计有助于发现具有特定对称性和特性的新材料.
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